The ‘Art’ of Predictive Analytics

Machine learning is not a science, it’s the new layer of business know-how that every company should have

By Gil Nizri

At the foundation of every business there are several operational tasks and practices that support the company’s existence and growth.

Common business disciplines are management, marketing, information technology and finance. Companies often use business disciplines differently, according to size, preferences and market needs.

The enormous amount of data that been collected by these companies contain a solid perspective on the way things work and don’t. The data usually contains a variety of practices that have been tested and validated during the years of a business’ existence. But the data on its own cannot reveal the best practices. Those are waiting to be revealed and replicated.

Nothing in life is free. To get these best practices, a company needs to first uncover, then map and automate these practices. Only then can they replicate previous successes or avoid previous failures.

The way of doing that is by harnessing machine learning and data science to turn these data points into actions. Algorithmic models are what give a company its action road map.

The Algorithm Economy
We are living in what has been termed the algorithm economy, which first revealed itself decades ago. Today, the algorithm economy is highlighted by organizations using predictive analytics to ask the right questions and find answers that provide business value.

The priority of what to do and where to search is influenced by the availability of the algorithm developers (also known as data scientists) on one hand, and the way the data science department is structured today, on the other. Usually the latter is a centralized elite unit that is a service provider to the entire organization.

The solution?
A company’s business intelligence team. It is members of this team that will be the predominant powerhouses in the modern algorithm economy. That’s because machine learning is inherently automated and having a Ph.D. in data science, statistics or math is no longer a prerequisite to generating accurate, data-driven and actionable analytics models.

It takes guts, executive sponsorship and a genuine disruptive solution to change the business culture and to become a leader.